from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(against_lib="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 prediction (58.33%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	e70d8e7886766d41f30889506baa1e67	2cf49829391aa7891e877f2ed070adf0	2.089471	0.179977	NaN	0.000383	0.002089	brute	-1	1	0.663	0.177449	0.002861	0.687	11.775048	11.776578	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	22d5eac4a4146149b35eae6914921514	2cf49829391aa7891e877f2ed070adf0	2.932399	0.052733	NaN	0.000273	0.002932	brute	-1	5	0.757	0.177626	0.002778	0.742	16.508855	16.510874	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	2.060044	0.005000	NaN	0.000388	0.002060	brute	1	100	0.882	0.216629	0.003587	0.875	9.509542	9.510845	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	0.021647	0.000338	NaN	0.000037	0.021647	brute	1	100	1.000	0.008676	0.000137	0.000	2.495138	2.495448	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	2.983173	0.040908	NaN	0.000268	0.002983	brute	-1	100	0.882	0.213105	0.003011	0.875	13.998639	14.000037	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	0.026092	0.003960	NaN	0.000031	0.026092	brute	-1	100	1.000	0.009111	0.001724	0.000	2.863663	2.914446	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	efac583623711d395ddae7a1e881ff68	2cf49829391aa7891e877f2ed070adf0	2.052457	0.007466	NaN	0.000390	0.002052	brute	1	5	0.757	0.177995	0.001188	0.742	11.530993	11.531249	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	b79d63bb7670c492de5c3befac58fe29	2cf49829391aa7891e877f2ed070adf0	1.194637	0.011474	NaN	0.000670	0.001195	brute	1	1	0.663	0.176718	0.001473	0.687	6.760124	6.760359	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	e70d8e7886766d41f30889506baa1e67	88843f54689e3271092f70126e1de585	1.865892	0.016249	NaN	0.000009	0.001866	brute	-1	1	0.896	0.026653	0.000271	0.967	70.007915	70.011521	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	22d5eac4a4146149b35eae6914921514	88843f54689e3271092f70126e1de585	2.716382	0.017446	NaN	0.000006	0.002716	brute	-1	5	0.922	0.028074	0.000364	0.974	96.757420	96.765533	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	16f94fc93942ff7ece860c9b0f64645f	88843f54689e3271092f70126e1de585	1.946333	0.004572	NaN	0.000008	0.001946	brute	1	100	0.929	0.063052	0.001949	0.975	30.868775	30.883518	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2d5f2b2c02d77766434d9e5033b4a76c	88843f54689e3271092f70126e1de585	2.893426	0.068410	NaN	0.000006	0.002893	brute	-1	100	0.929	0.062901	0.002982	0.975	45.999459	46.051116	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	efac583623711d395ddae7a1e881ff68	88843f54689e3271092f70126e1de585	1.932452	0.005308	NaN	0.000008	0.001932	brute	1	5	0.922	0.028430	0.000342	0.974	67.971399	67.976310	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	b79d63bb7670c492de5c3befac58fe29	88843f54689e3271092f70126e1de585	1.058217	0.004668	NaN	0.000015	0.001058	brute	1	1	0.896	0.027144	0.000440	0.967	38.985523	38.990654	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.001	6.860	0.0	-1	1	0.048	0.003	0.242	0.243	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.828	0.0	-1	5	0.047	0.000	0.249	0.249	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.827	0.0	1	100	0.047	0.000	0.251	0.251	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.892	0.0	-1	100	0.047	0.000	0.247	0.247	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	6.970	0.0	1	5	0.047	0.000	0.245	0.245	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.018	0.0	1	1	0.050	0.006	0.230	0.232	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.374	0.0	-1	1	0.009	0.000	0.497	0.497	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.375	0.0	-1	5	0.009	0.000	0.497	0.497	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.378	0.0	1	100	0.008	0.000	0.505	0.505	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.377	0.0	-1	100	0.008	0.000	0.507	0.507	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.357	0.0	1	5	0.009	0.000	0.525	0.525	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.373	0.0	1	1	0.009	0.000	0.496	0.496	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.089	0.180	0.000	0.002	-1	1	0.177	0.003	11.775	11.777	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.002	0.000	0.025	-1	1	0.009	0.000	2.840	2.841	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.932	0.053	0.000	0.003	-1	5	0.178	0.003	16.509	16.511	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.002	0.000	0.025	-1	5	0.009	0.000	2.875	2.875	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.060	0.005	0.000	0.002	1	100	0.217	0.004	9.510	9.511	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.000	0.000	0.022	1	100	0.009	0.000	2.495	2.495	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.983	0.041	0.000	0.003	-1	100	0.213	0.003	13.999	14.000	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.026	0.004	0.000	0.026	-1	100	0.009	0.002	2.864	2.914	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.052	0.007	0.000	0.002	1	5	0.178	0.001	11.531	11.531	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.000	0.000	0.021	1	5	0.009	0.000	2.350	2.350	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.195	0.011	0.001	0.001	1	1	0.177	0.001	6.760	6.760	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.002	0.000	0.020	1	1	0.009	0.000	2.214	2.214	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.866	0.016	0.000	0.002	-1	1	0.027	0.000	70.008	70.012	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.005	0.000	0.006	-1	1	0.001	0.000	9.212	9.285	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.716	0.017	0.000	0.003	-1	5	0.028	0.000	96.757	96.766	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.009	0.002	0.000	0.009	-1	5	0.001	0.000	12.795	12.919	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.946	0.005	0.000	0.002	1	100	0.063	0.002	30.869	30.884	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	3.998	4.021	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.893	0.068	0.000	0.003	-1	100	0.063	0.003	45.999	46.051	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.009	0.003	0.000	0.009	-1	100	0.001	0.000	12.051	12.132	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.932	0.005	0.000	0.002	1	5	0.028	0.000	67.971	67.976	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.120	4.142	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.058	0.005	0.000	0.001	1	1	0.027	0.000	38.986	38.991	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.622	2.638	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	fe8267e25dadb302ed498e50fabf7ef4	1eb6cf1a720a225efb91df0b529b0510	0.844469	0.976965	NaN	0.000095	0.000844	kd_tree	-1	1	0.929	0.116938	0.002629	0.910	7.221501	7.223326	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	dc74b969bf622bc24ba3bc62c980983b	1eb6cf1a720a225efb91df0b529b0510	1.026248	0.294924	NaN	0.000078	0.001026	kd_tree	-1	5	0.946	0.215462	0.010701	0.941	4.763016	4.768886	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	41accfcdcd5c50784bedf6164b63de99	1eb6cf1a720a225efb91df0b529b0510	6.051497	0.298986	NaN	0.000013	0.006051	kd_tree	1	100	0.951	0.635232	0.007550	0.940	9.526433	9.527105	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	022f7445d43bb1dbc24dc3106c03cb93	1eb6cf1a720a225efb91df0b529b0510	3.220000	0.165930	NaN	0.000025	0.003220	kd_tree	-1	100	0.951	0.629795	0.008164	0.940	5.112778	5.113208	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	84622ba45e941db642965553529e1941	1eb6cf1a720a225efb91df0b529b0510	1.731796	0.218939	NaN	0.000046	0.001732	kd_tree	1	5	0.946	0.219942	0.002896	0.941	7.873867	7.874549	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	a9455565db1d8e052a783317c99744ff	1eb6cf1a720a225efb91df0b529b0510	0.940384	0.253248	NaN	0.000085	0.000940	kd_tree	1	1	0.929	0.119233	0.001555	0.910	7.886956	7.887627	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	fe8267e25dadb302ed498e50fabf7ef4	edf7cb014c2c0910e1c3aaaf8ae633f2	0.024905	0.011997	NaN	0.000642	0.000025	kd_tree	-1	1	0.891	0.000461	0.000106	0.879	54.046726	55.446886	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	dc74b969bf622bc24ba3bc62c980983b	edf7cb014c2c0910e1c3aaaf8ae633f2	0.022914	0.000642	NaN	0.000698	0.000023	kd_tree	-1	5	0.911	0.000632	0.000022	0.905	36.237670	36.260426	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	41accfcdcd5c50784bedf6164b63de99	edf7cb014c2c0910e1c3aaaf8ae633f2	0.032740	0.000160	NaN	0.000489	0.000033	kd_tree	1	100	0.894	0.004484	0.000024	0.917	7.301651	7.301757	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	022f7445d43bb1dbc24dc3106c03cb93	edf7cb014c2c0910e1c3aaaf8ae633f2	0.035401	0.004854	NaN	0.000452	0.000035	kd_tree	-1	100	0.894	0.005187	0.001434	0.917	6.824418	7.080241	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	84622ba45e941db642965553529e1941	edf7cb014c2c0910e1c3aaaf8ae633f2	0.019906	0.000118	NaN	0.000804	0.000020	kd_tree	1	5	0.911	0.000632	0.000019	0.905	31.498341	31.511919	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	a9455565db1d8e052a783317c99744ff	edf7cb014c2c0910e1c3aaaf8ae633f2	0.018534	0.000095	NaN	0.000863	0.000019	kd_tree	1	1	0.891	0.000381	0.000025	0.879	48.622914	48.731180	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.520	0.075	0.023	0.0	-1	1	0.774	0.027	4.548	4.551	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.918	0.057	0.020	0.0	-1	5	0.768	0.018	5.105	5.106	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.848	0.097	0.021	0.0	1	100	0.743	0.007	5.177	5.177	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.423	0.076	0.018	0.0	-1	100	0.764	0.017	5.788	5.790	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.274	0.043	0.019	0.0	1	5	0.760	0.018	5.622	5.624	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.853	0.057	0.021	0.0	1	1	0.780	0.011	4.942	4.942	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.021	0.0	-1	1	0.003	0.002	0.229	0.257	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.032	0.0	-1	5	0.002	0.002	0.241	0.298	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.032	0.0	1	100	0.001	0.001	0.459	0.542	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	-1	100	0.001	0.000	0.633	0.634	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.032	0.0	1	5	0.001	0.000	0.641	0.642	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	1	0.001	0.000	0.639	0.639	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.844	0.977	0.000	0.001	-1	1	0.117	0.003	7.222	7.223	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.000	0.000	0.003	-1	1	0.000	0.000	9.969	10.490	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.026	0.295	0.000	0.001	-1	5	0.215	0.011	4.763	4.769	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	7.523	7.978	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	6.051	0.299	0.000	0.006	1	100	0.635	0.008	9.526	9.527	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	1	100	0.001	0.000	6.411	6.841	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.220	0.166	0.000	0.003	-1	100	0.630	0.008	5.113	5.113	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	6.489	6.919	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.732	0.219	0.000	0.002	1	5	0.220	0.003	7.874	7.875	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	5	0.000	0.000	3.603	3.879	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.940	0.253	0.000	0.001	1	1	0.119	0.002	7.887	7.888	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.762	4.022	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.025	0.012	0.001	0.000	-1	1	0.000	0.000	54.047	55.447	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	23.760	25.595	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.023	0.001	0.001	0.000	-1	5	0.001	0.000	36.238	36.260	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	23.027	24.388	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.033	0.000	0.000	0.000	1	100	0.004	0.000	7.302	7.302	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	5.972	6.243	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.035	0.005	0.000	0.000	-1	100	0.005	0.001	6.824	7.080	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	20.358	22.164	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.020	0.000	0.001	0.000	1	5	0.001	0.000	31.498	31.512	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	6.730	7.129	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.019	0.000	0.001	0.000	1	1	0.000	0.000	48.623	48.731	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	6.749	7.117	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.518	0.068	30	0.031	0.0	random	0.379	0.025	1.364	1.367	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.563	0.014	30	0.028	0.0	k-means++	0.408	0.025	1.380	1.382	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.655	0.192	30	0.141	0.0	random	2.610	0.024	2.166	2.166	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.908	0.029	30	0.135	0.0	k-means++	2.751	0.010	2.147	2.148	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.011	0.000	random	0.0	0.0	10.996	12.760	See	See
2	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	random	0.0	0.0	8.501	14.001	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.010	0.000	k-means++	0.0	0.0	12.100	14.117	See	See
5	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	k-means++	0.0	0.0	13.440	14.412	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.467	0.000	random	0.0	0.0	7.215	7.776	See	See
8	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	random	0.0	0.0	13.412	13.927	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.486	0.000	k-means++	0.0	0.0	6.841	7.436	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	0.0	0.0	13.611	13.900	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 prediction (50.0%). The observed differences can be found in the diff_adjusted_rand_scores column. The chosen difference threshold is 0.001 for adjusted_rand_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	3db9c030f488d0e8ff350ae449da4627	058b7e3842b587a5c675518f0706f5ee	0.001739	0.000131	20	0.009203	0.000002	full	random	20	300	1	1.000000e-16	0.000126	0.000416	0.000037	-0.000965	4.181872	4.198247	0.001090
4	KMeans_short	predict	10000	1000	2	ac9b8cec4749455c0d1cd1a92c150716	058b7e3842b587a5c675518f0706f5ee	0.001763	0.000150	20	0.009077	0.000002	full	k-means++	20	300	1	1.000000e-16	0.001245	0.000435	0.000052	-0.000750	4.047999	4.077106	0.001995
7	KMeans_short	predict	10000	1000	100	3db9c030f488d0e8ff350ae449da4627	7f85b913f395ec54101a6738ea63a9a7	0.002409	0.000244	20	0.332044	0.000002	full	random	20	300	1	1.000000e-16	0.278733	0.000953	0.000072	0.293767	2.529253	2.536369	0.015034
10	KMeans_short	predict	10000	1000	100	ac9b8cec4749455c0d1cd1a92c150716	7f85b913f395ec54101a6738ea63a9a7	0.002428	0.000230	20	0.329499	0.000002	full	k-means++	20	300	1	1.000000e-16	0.317011	0.000949	0.000090	0.256968	2.559753	2.571223	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.074	0.000	20	0.002	0.0	random	0.026	0.001	2.831	2.835	See	See
3	KMeans_short	fit	10000	10000	2	0.207	0.001	20	0.001	0.0	k-means++	0.081	0.000	2.559	2.559	See	See
6	KMeans_short	fit	10000	10000	100	0.198	0.012	20	0.040	0.0	random	0.106	0.001	1.856	1.856	See	See
9	KMeans_short	fit	10000	10000	100	0.560	0.008	20	0.014	0.0	k-means++	0.296	0.003	1.894	1.894	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	random	0.000	0.0	4.182	4.198	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	0.000	0.0	13.039	13.373	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	k-means++	0.000	0.0	4.048	4.077	See	See
5	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	0.000	0.0	12.301	12.670	See	See
7	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.332	0.000	random	0.001	0.0	2.529	2.536	See	See
8	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	0.000	0.0	9.975	10.204	See	See
10	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.329	0.000	k-means++	0.001	0.0	2.560	2.571	See	See
11	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	k-means++	0.000	0.0	10.935	11.297	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	f11be2529b0a77bc8fb5ee8de141e6b2	09c36ed6521e03974d0c134895f56c01	0.000370	0.000425	[20]	2.163715	3.697344e-07	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.56	0.000697	0.001145	0.55	0.530832	1.021726	0.01
4	LogisticRegression	predict	1000	100	10000	f11be2529b0a77bc8fb5ee8de141e6b2	7daecaea01e5baa8c862f264e3c1c3b0	0.001694	0.000324	[26]	4.722577	1.693990e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.35	0.003262	0.001312	0.28	0.519353	0.559785	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.072	0.365	[20]	0.072	0.000	2.011	0.034	5.507	5.507	See	See
3	LogisticRegression	fit	1000	1000	10000	0.983	0.596	[26]	0.081	0.001	0.992	0.034	0.990	0.991	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	2.164	0.0	0.001	0.001	0.531	1.022	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.017	0.0	0.000	0.000	0.361	0.366	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	4.723	0.0	0.003	0.001	0.519	0.560	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	1.071	0.0	0.001	0.000	0.120	0.120	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). The observed differences can be found in the diff_r2_scores column. The chosen difference threshold is 0.001 for r2_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup	diff_r2_scores
1	Ridge	predict	1000	1000	10000	a70ae8dc5c059ec88cf27c61016aee2a	14fae294d530397144c65ba3209bf125	0.011618	0.000181	NaN	6.886163	0.000012	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.082567	0.018498	0.000304	0.122191	0.628034	0.628119	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.182	0.003	0.439	0.0	0.193	0.001	0.942	0.942	See	See
3	Ridge	fit	1000000	1000000	100	1.130	0.074	0.708	0.0	0.308	0.265	3.672	4.844	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.012	0.0	6.886	0.0	0.018	0.0	0.628	0.628	See	See
2	Ridge	predict	1000	1	10000	0.000	0.0	1.411	0.0	0.000	0.0	0.594	0.646	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.0	6.067	0.0	0.000	0.0	0.457	0.683	See	See
5	Ridge	predict	1000000	1	100	0.000	0.0	0.017	0.0	0.000	0.0	0.582	0.613	See	See